Title: A hybrid model of empirical wavelet transform and extreme learning machine for dissolved oxygen forecasting
Authors: Juan Huan; Weijian Cao; Yuwan Gu; Yilin Qin
Addresses: Changzhou University, Changzhou, Jiangsu, China ' Changzhou University, Changzhou, Jiangsu, China ' Changzhou University, Changzhou, Jiangsu, China ' Department of Creative Information, Changzhou Technical Institute of Tourism and Commerce University, Changzhou, Jiangsu, China
Abstract: The accurate predicting trend of dissolved oxygen (DO) can reduce the risks to aquaculture, so a combined nonlinear prediction model based on empirical wavelet transform (EWT) and extreme learning machine (ELM) optimised by adaptive disturbance particle swarm optimisation (ADPSO) is proposed. First of all, DO series are decomposed into a term of relatively subsequence by EWT, secondly, the decomposed components are reconstructed using the C-C method, and thirdly an ELM prediction model of every component is established. At last, the predicted values of DO datasets are calculated by using RBF to reconstruct the forecasting values of all components. This model is tested in the special aquaculture farm in Liyang City, Jiangsu Province. Results indicate that the proposed prediction model of EWT-ELM has better performance than WD-ELM, EMD-ELM, ELM and EWT-BP. The research shows that the combined forecasting model can effectively extract the sequence characteristics, and can provide a basis for decision-making management of water quality, which has certain application value.
Keywords: water quality prediction; hybrid model; dissolved oxygen; empirical wavelet transform; EWT.
International Journal of Embedded Systems, 2020 Vol.13 No.1, pp.9 - 17
Received: 26 Oct 2018
Accepted: 20 Jan 2019
Published online: 11 May 2020 *